Metadata-Version: 2.1
Name: mlglass
Version: 0.0.1
Summary: MLglass: A Transparency with models
Home-page: https://github.com/Nikeshbajaj/mlglass
Author: Nikesh Bajaj
Author-email: bajaj.nikey@gmail.com
License: MIT
Download-URL: https://github.com/Nikeshbajaj/mlglass/tarball/0.0.1
Description: # Machine Learning Models with glass (Transparency)
        
        ### Links: **[Github](https://github.com/Nikeshbajaj/mlglass)**  |  **[PyPi - project](https://pypi.org/project/mlglass/)**
        ### Installation: *[pip install spkit](https://pypi.org/project/mlglass/)*
        
        
        -----
        ## Table of contents
        -[Logistic Regression](#logistic-regression---view-in-notebook)
        -[Naive Bayes](#naive-bayes---view-in-notebook)
        -[Decision Trees](#decision-trees---view-in-notebook)
        -----
        
        
        ## Installation
        
        **Requirement**:  numpy, matplotlib
        
        ### with pip
        
        ```
        pip install mlglass
        ```
        
        ### Build from the source
        Download the repository or clone it with git, after cd in directory build it from source with
        
        ```
        python setup.py install
        ```
        
        #### Machine Learning models - with visualizations
        * Logistic Regression
        * Naive Bayes
        * Decision Trees
        * DeepNet (to be updated)
        
        
        ## Machine Learning
        ### [Logistic Regression](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.1_LogisticRegression_examples.ipynb) - *View in notebook*
        <p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/LogisticRegression/img/example5.gif" width="600"/></p>
        
        ### [Naive Bayes](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.2_NaiveBayes_example.ipynb) - *View in notebook*
        <p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/Probabilistic/img/FeatureDist.png" width="600"/></p>
        
        ### [Decision Trees](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.3_Tree_Example_Classification_and_Regression.ipynb) - *View in notebook*
        
        [**[source code]**](https://github.com/Nikeshbajaj/spkit/blob/master/examples/trees_example.py) | [**[jupyter-notebook]**](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.3.1_Trees_Classification_Example.ipynb)
        <p align="center">
        <img src="https://raw.githubusercontent.com/Nikeshbajaj/spkit/master/figures/tree_sinusoidal.png" width="800"/>
        <img src="https://raw.githubusercontent.com/Nikeshbajaj/spkit/master/figures/trees.png" width="800"/>
        </p>
        
        
        #### Plottng tree while training
        
        <p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/Trees/img/a123_nik.gif" width="600"/></p>
        
        [**view in repository **](https://github.com/Nikeshbajaj/spkit/tree/master/notebooks)
        
        ______________________________________
        
        # Contacts:
        
        * **Nikesh Bajaj**
        * http://nikeshbajaj.in
        * n.bajaj@qmul.ac.uk
        * n.bajaj@uel.ac.uk
        * bajaj.nikkey@gmail.com
        ### PhD from Queen Mary University of London, Postdoctoral at University of East London
        ______________________________________
        
Keywords: Machine Learning,Visualizations,Weights,Decision Tree,Naive Bayes
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 5 - Production/Stable
Description-Content-Type: text/markdown
